1. Field of The Invention
The present invention relates generally to linguistic applications or attribute matching in information retrieval and data processing and, more particularly, to systems or methods for measuring similarities between words or between representations of multiple attributes of products or items, especially words or attributes related to pharmacological products or items.
2. Related Art
Errors in the administration of medications, such as occur when the wrong drug or the wrong dosage are provided to a patient, represent a serious problem that has been much discussed by health professionals, patient welfare groups, academics, insurers, and others. Various causes for these errors have been identified, including the misunderstanding of physicians"" orders due to illegible handwriting, similarity between drug names, confusing pharmaceutical packaging, poor design of devices for administering drugs, and other factors. An overview of some systemic causes of medication errors is provided in M. R. Cohen, xe2x80x9cDrug product characteristics that foster drug-use-system errors,xe2x80x9d 52 Am. J. Health-Syst Pharm (February 1995) pp. 395-399, hereafter referred to as xe2x80x9cthe Cohen article,xe2x80x9d which is hereby incorporated by reference in its entirety. Another overview of the subject is found in M. R. Cohen (ed.), Medication errors, American Pharmaceutical Association, Washington, D.C. (1999).
A variety of groups and government agencies have programs designed to identify the sources of medication errors and to reduce the likelihood of their occurrence. For example, the American Society of Hospital Pharmacists has issued xe2x80x9cASHP guidelines on preventing medication errors in hospitals,xe2x80x9d 50 Am. J. Hosp. Pharm. (1993) pp.305-314; the U.S. Food and Drug Administration (FDA) has established a Subcommittee on Medication Errors; the National Coordinating Council for Medication Error Reporting and Prevention has information available on the Internet and elsewhere; and a medication-error reporting network has been established by the non-profit Institute for Safe Medication Practices and the Drug Product Problem Reporting Network of the U.S. Pharmacopeia, Inc. (USP).
One class of errors that has been identified and studied by these groups and agencies is related to the use of drug names that sound like, and/or look like, other drug names. Lists of these sound-alike or look-alike drugs have been published, as in N. M. Davis, et al., xe2x80x9cLook-alike and sound-alike drug names: the problem and the solution,xe2x80x9d 27 Hosp. Pharm. (1992) pp. 95-98, 102-105, 108-110; and N. M. Davis, xe2x80x9cDrug names that look and sound alike,xe2x80x9d in Hospital Pharmacy, vol. 32, pages 1558-70 (1997). Agencies such as the FDA, the United States Adopted Names Council (USAN), the International Nonproprietary Name (INN) Committee of the World Health Organization, the European Agency for the Evaluation of Medicinal Products (EMEA), and the U.S. Patent and Trademark Office (USPTO), have regulations and programs related to the possibility of confusion among drug names. Also, pharmaceutical companies typically expend significant effort in proposing and perfecting trademarks for new drugs.
Notwithstanding the activities of these, and other, organizations, new drugs continue to be given names that may be confused with those of existing drugs, not infrequently leading to serious or fatal consequences for patients. Confusion between Celebrex(copyright) and Celexa(copyright) is a recent example, as documented in USP Quality Review, May 1999, no. 66 (U.S. Pharmacopeia, Rockville, Md.). Also, existing look-alike or sound-alike drug names remain on the market. One reason for these continuing problems is the diverse, and sometimes conflicting, goals of the agencies and companies involved in the naming of drugs. For example, pharmaceutical companies seek trademarks based not just on the objective of distinguishing their drugs from the competition, but also on enhancing recognition and recall and creating brand loyalty. The USAN and the INN, although concerned with name confusion, are also interested in ensuring that drug names are useful to health care professionals, i.e., that drug names preferably convey some medical information rather than being merely arbitrary or fanciful. Similarly, the USP has an interest in encouraging the use of drug names that are consistent with the existing compendial nomenclature. In contrast, one element used by the courts and the USPTO to determine the likelihood of confusion between trademarks is the strength of a mark. A mark may be strong, and therefor entitled to broad protection, because it has a relatively remote relationship with the product, such as a mark that is arbitrary or fanciful.
Another reason for the continuing problem of drug name confusion is attributable simply to the large number of drugs available. For example, there are over 15,000 medications sold in the United States alone, and there are over 35,000 names in the U.S. Patent and Trademark Office""s database of trademark registrations for pharmaceuticals. Approximately half a million pharmaceutical trademarks are registered in the major industrialized countries. Even agencies, such as the FDA, that are focused squarely on reducing medication errors due to name confusion are hard pressed to anticipate sources of name confusion due to the large number of pairs of proposed and existing names, proposed and proposed names, or existing and existing names.
Moreover, assessment of the likelihood of drug-name confusion often is limited by reliance on the subjective judgment of human experts. For example, the FDA employs panels of experts who are directed to make their evaluations based on guidelines that generally are open to subjective interpretation. The inevitable disagreements that arise result in what social scientists commonly refer to as xe2x80x9cpoor interrater reliability.xe2x80x9d Similarly, practitioners before the USPTO, and the examiners and other officials of that agency, must apply complex guidelines (statutory, regulatory, and judicial) that call ultimately for the application of subjective judgments.
Efforts have been made to systematize the analysis of drug names by human experts. For example, the Cohen article refers to a system used by the pharmaceutical industry xe2x80x9cfor assessing proposed trademarks for possible medication-error problems.xe2x80x9d 52Am. J. Health-Syst Pharm (February 1995) p. 398. More generally, the same article refers to xe2x80x9ca system for ranking pharmaceutical labeling and packaging for error potential.xe2x80x9d Id. at p. 399. Both systems appear to be based on the participation of experts who, in accordance with an evaluation protocol, apply conventional social-science rating techniques to some factors that are considered to be relevant to the potential for errors. For example, experts pronounce product names read from handwritten drug orders from physicians, and rank the potential for confusion on a scale of one to ten. Other experts assign point values to each factor. These scores for each factor may then be combined to provide an overall rating between one and ten that is intended to be indicative of the potential for confusion. Id. at p. 398. Although a quantitative rating is thus produced, this approach relies on the subjective judgment of experts. As is evident, the judgment of any person may vary on the same subject from one trial to another, and the judgments of two people may vary on the same subject. Thus, the approach is not deterministic in the sense that a particular input (a handwritten drug order, for example) may produce one output (the quantitative rating) for one trial and the same input may produce another output for another trial. Also, the approach is not xe2x80x9cautomaticxe2x80x9d in the sense that determination of the quantitative rating requires human involvement.
Some computer-implemented techniques have been employed to provide a more objective, and automated, analysis of drug-name similarity. For example, pharmaceutical companies typically screen potential new drug names by computerized searching, apparently based on similarity of spelling and/or sound of the new drug names as compared with existing drug names. Typically, however, regulatory agencies do not require results of these searches to be submitted as part of the evaluation process. Trademark attorneys and commercial trademark-searching firms similarly use computer-based searching techniques, including Internet searching, to assess the likelihood that a registration for a proposed drug name will be granted by the USPTO. The utility to public agencies and the public of these search techniques is limited, however, by the fact that the precise methods by which the searches are conducted generally are not publicly disclosed. Consequently, due to this lack of transparency, and due to uncertainty as to whether the same or similar standards are applied to one or more searches by one or more firms, comparisons of the likelihood of confusion across a wide population of drugs is problematic or impracticable.
Moreover, these automated conventional techniques have various characteristics that limit their efficacy in reducing confusion among drug names. For example, most of these techniques provide only an approximate relative measure. That is, the searching techniques may simply rank reference names in order of similarity to a target name. Thus, with respect to a target drug named xe2x80x9cAAA,xe2x80x9d reference drug xe2x80x9cAAxe2x80x9d may be ranked first, reference drug xe2x80x9cAxe2x80x9d second, reference drug xe2x80x9cABxe2x80x9d third, and so on. Moreover, if additional quantitative information is provided, it may be limited to a simple score that is not tied to a benchmark. That is, according to some conventional techniques, one pair of target and reference drugs may have a score of xe2x80x9c1.2xe2x80x9d and another pair a score of xe2x80x9c0.7,xe2x80x9d but neither score provides any absolute measure of the likelihood of confusion. Rather, only a relative measure is provided by these techniques. That is, a score of 1.2 may indicate a higher likelihood of confusion, based on the names of the drugs, than a score of 0.7.
The present invention includes an apparatus, method, or product for comparing pharmaceutical drugs based on multiple attributes of the drugs. That is, comparisons between or among drugs may be based not just on the names of the drugs, as is typical in many conventional systems, but also on a number of other attributes of the drug product. These other attributes are collectively referred to herein for convenience as xe2x80x9cproduct attributes,xe2x80x9d as distinguished from a drug""s xe2x80x9cname attribute.xe2x80x9d For example, product attributes may include the dosage strengths of the drugs, their route of administration, or their color or shape. Additional specific examples are described below. A reason to include the product attributes of drugs in addition to their name attributes is that medication errors may arise not only because two drugs have similar names, but also because they physically resemble each other, are administered in a similar way, are made by the same company and thus have similar packaging or other similarities, and so on. By taking these additional attributes into account, the present invention thus provides substantial advantages not provided by conventional techniques that rely solely on a comparison of name attributes.
As used herein, the word xe2x80x9cdrugxe2x80x9d will be understood to have a meaning that is possibly much broader than its ordinary dictionary meaning as applied to pharmaceutical drugs. In particular, xe2x80x9cdrugxe2x80x9d will be understood to refer herein to any pharmaceutical drug, any biological preparation, and to any word or phrase (irrespective of the mode of communication), device, product, substance, method, procedure, or other item that may be the subject of medical communications, such as prescriptions. For example, a prescription may be written for a device, such as a hypodermic needle, or a product, such as a bandage. The confusion of one device for another device, or one product for another product, may have consequences that are as severe as the confusion of one pharmaceutical drug for another. Many of these devices, products, and so on, have both name and product attributes so that comparisons may be made for them, in accordance with the present invention, in essentially the same manner as comparisons may be made with respect to pharmaceutical drugs.
Moreover, the present invention may also be applied to deterministically provide quantitative comparisons between devices, products, substances, methods, procedures, and other items, that are not ordinarily the subject of medical communications. As one non-limiting example, the present invention may be used to quantify the likelihood of confusion between a product name related to a consumer Internet application with a product name related to a software application for use by developers of database software. The names of these illustrative products are not the only attributes that may be operated upon in accordance with the invention. Other xe2x80x9cproductxe2x80x9d attributes may include, for example, the type of customer for whom the products are designed (i.e., consumers and software developers in this example), the type of market into which the products are sold (computer software in both cases), and so on.
The word xe2x80x9cnamexe2x80x9d is used broadly herein. For example, a drug may have one or more generic names, chemical names, proprietary brand names, non-proprietary names, abbreviations, informal names, and so on. For background information in this regard, see D. Boring, xe2x80x9cThe development and adoption of nonproprietary, established, and proprietary names for pharmaceuticals,xe2x80x9d in Drug Information Journal, vol. 31, pages 621-634 (1997). Generally, the xe2x80x9cnamexe2x80x9d of a drug, as used herein, may refer to any one or more words or symbols that are used to identify or describe a drug.
Comparisons generally are described herein as being made between one or more xe2x80x9ctargetxe2x80x9d drugs and one or more xe2x80x9creferencexe2x80x9d drugs. In one situation, a single target drug is compared to a single reference drug by analyzing their name and product attributes. The target drug may then similarly be compared to another reference drug, and so on through a list of reference drugs. These xe2x80x9cone-to-onexe2x80x9d comparisons may then be repeated for another target drug in relation to each of the reference drugs. In other situations, the attributes of a single target drug may be compared to composite attributes of two or more reference drugs; i.e., a xe2x80x9cone-to-manyxe2x80x9d form of comparison. Similarly, the composite attributes of two or more target drugs may be compared to a single reference drug in a xe2x80x9cmany-to-onexe2x80x9d form of comparison, or to the composite attributes of two or more reference drugs in a xe2x80x9cmany-to-manyxe2x80x9d form of comparison. For convenience, references typically are made herein simply to the one-to-one form of comparison, although it will be understood that these references generally could alternatively be stated in terms of any of the other forms. For example, the term xe2x80x9ctarget-reference pairxe2x80x9d may be used herein with reference to a one-to-one form of comparison of the attributes of an illustrative single target drug with an illustrative single reference drug. However, this term should be understood to also implicitly constitute an illustration of the other forms. For example, the term xe2x80x9ctarget-reference pairxe2x80x9d generally may also apply to the one-to-many form of comparison of the attributes of a single target drug with the composite attributes of two or more reference drugs. Similarly, for example, a description directed to a singular xe2x80x9creference drugxe2x80x9d of the target-reference pair should be understood to include, in the illustrative alternative case of the one-to-many form, two or more reference drugs.
In some applications, such as when it is proposed to introduce a new drug to the market, the target drug may be the proposed new drug and the reference drug may be selected from a list of existing drugs. However, it need not be so; both drugs may be new drugs, both may be existing drugs, the target drug may be old and the reference drug new, or either may be categorized in a different way.
The word xe2x80x9ccomparisonxe2x80x9d and its grammatical variants, will be understood to have a broad meaning as used herein. Thus, a xe2x80x9ccomparisonxe2x80x9d may include processes such as statistical correlation; adaptive and other types of association; application of any mathematical or logical functions, including those derived analytically or those that may be learned from examples or by other adaptive methods (such as decision trees and any other learning method); or any other similar process or form of analysis that is now known or that may be developed in the future.
Moreover, a comparison of drugs need not be limited to the purpose of assessing their similarity or, alternatively stated, the likelihood of confusion between them. For example, in some aspects of the invention, the target-reference pair may also be compared in order to determine a measure of the severity of confusion. For example, if the target-reference pair consists of information related to drugs A and B, and the erroneous substitution of drug B for drug A is likely to have fatal consequences, then there is a high measure of severity of confusion. In some implementations, the severity of confusion is combined with a measure of the similarity of the name and product attributes of the target-reference pair to provide a xe2x80x9cseverity-weighted similarity score.xe2x80x9d In other implementations, separate scores may be provided for the severity of confusion, and for the similarity, of each target-reference pair. In either case, the present invention has the additional advantage over conventional systems of providing a quantitative measure of the consequences of confusion. This measure may be integrated, or juxtaposed, with a quantitative measure of the likelihood of confusion.
Aspects of the invention also include a composite score generator that operates on the similarity scores, and optionally operates on the severity of confusion scores, to provide a composite quantitative comparison between the target and reference drugs. More specifically, the term xe2x80x9ccomposite quantitative comparisonxe2x80x9d is used herein to refer to one or more quantitative measures of the similarity, and optionally of the severity of confusion, of a pair of target and reference drugs. This quantitative output of the invention is, at the least, a valuable adjunct to the exercise of human judgment. That is, even in those applications in which composite quantitative comparisons are provided to human experts for further processing or analysis, rather than relied upon directly, the decision-making process of the experts is greatly facilitated by the availability of objective information that has been generated deterministically and assembled based on a variety of relevant factors.
More specifically, some embodiments of the present invention (referred to hereafter as the xe2x80x9capparatusxe2x80x9d embodiments), are directed to a drug comparator for comparing one or more target drugs with one or more reference drugs based on attribute data that describes a plurality of attributes of each target drug and each reference drug. The drug comparator includes a product attribute comparator that generates one or more product-attribute similarity scores representing a similarity of product attributes of a selected one or more target drugs and a selected one or more reference drugs. The drug comparator also includes a composite score generator that generates one or more composite quantitative comparisons based, at least in part, on one or more attribute similarity scores comprising the one or more product-attribute similarity scores.
The one or more composite quantitative comparisons are deterministic in typical implementations of these embodiments. The term xe2x80x9cdeterministicxe2x80x9d is used in this context generally to mean that the outputs of the system or method of the present invention vary only if the inputs to the system vary. In more specific terms, an example may be assumed in which a user selects a particular drug A to be the target drug and drug B to be the reference drug. Because the system or method is deterministic, the one or more composite quantitative comparisons that are generated will be the same each time drugs A and B are selected, provided that there is no change in the input data regarding the attributes of the target and reference drugs (referred to herein as xe2x80x9ctarget-reference dataxe2x80x9d). Thus, comparisons of the same drugs are replicable by different users and over periods of time. Similarly, a measure of the similarity, or of the severity of confusion, of a particular target-reference pair may be reliably compared to corresponding measures generated with respect to other target-reference pairs. That is, the results of these comparisons will not vary from one user to the next, or one trial to the next (if the target-reference data remain the same and the functions or steps of the system or method have not been changed). Another important benefit of this deterministic property is that regulators may make an objective assessment of the utility of the comparisons produced. If the comparisons are found to be helpful in identifying certain drugs subject to confusion (and optionally of having severe consequences of confusion), then the deterministic nature of the process used to generate these comparisons provides a degree of assurance that other confusing drugs will similarly be identified. This assurance generally is not present in non-deterministic systems in which human judgement, or other unpredictable factors, may influence the outcome.
A similar advantage of some embodiments of the present invention as compared to conventional systems and methods is that transparency and standardization are possible. Transparency in this context means that functions or steps in accordance with embodiments of the present invention generally may be precisely described and disclosed. Thus, users may form their own judgments as to how those functions or steps operate and whether they adequately take into account and/or process relevant information. Standardization means that, because of transparency, users may be confident that the same or equivalent functions or steps have been used to determine likelihood and/or severity of confusion from one target-reference pair to another, or between trials of the same target-reference pair.
After a user selects target and reference drugs, the one or more composite quantitative comparisons typically are automatically generated. That is, they may be generated in some implementations of the invention without any further involvement of the user. The present invention thus provides an advantage over conventional systems or methods that are not automated in this sense because the processing of the inputs to provide an output involves the use of humans to analyze facts and/or make decisions. Particularly when the human involvement is in the form of highly paid and busy experts, the savings in money and time due to automatic operation may be substantial. This advantage may be particularly important when a large number of target-reference pairs are involved. In particular, the system or method of the present invention generally provides a practical way for comprehensively comparing very large numbers of target and reference drugs. Similarly, comparisons of a particular target-reference pair with a large population of other target-reference pairs are possible due to automatic operation, typically carried out by a computer system. In contrast, a comprehensive comparison of large numbers of target and reference drugs may not be practicable using conventional systems or methods because of the time and/or expense involved.
In some further implementations, the invention includes a name attribute comparator that generates name-attribute similarity scores representing a similarity of name attributes of the selected target and reference drugs. The name-attribute similarity scores are a type of attribute similarity scores; that is, the attribute similarity scores may be made up partially, or entirely, of name-attribute similarity scores. In some aspects, the name attribute comparator includes an orthographic analyzer that generates a name-attribute similarity score based at least in part on one or more comparisons between orthographic representations of the names of the selected target and reference drugs. These comparisons may be based on N-gram measures and/or edit distance measures of orthographic similarity. In some aspects, the name attribute comparator may include a phonetic analyzer that generates a name-attribute similarity score based at least in part on phonetic measures of the names of the target and reference drugs. The term xe2x80x9cphonetic measuresxe2x80x9d is used herein as a convenient way of indicating that phonetic transcription techniques are employed, and then a measure of orthographic similarity, such as N-gram measures or edit distance measures, are applied to the phonetically transformed representations. Also, in further aspects, the name attribute comparator includes a phonological analyzer that generates a name-attribute similarity score based at least in part on phonological measures of the names of the target and reference drugs. In some aspects, the name attribute comparator may have an orthographic analyzer, a phonetic analyzer, and a phonological analyzer.
In some implementations, the product attribute comparator of the drug comparator may include any combination of one or more of the following elements:
a strength comparator that generates a product-attribute similarity score based at least in part on the dosage strengths of the target and reference drugs;
an indication comparator that generates a product-attribute similarity score based at least in part on the indicated uses of the target and reference drugs;
a dosage form comparator that generates a product-attribute similarity score based at least in part on the dosage forms of the target and reference drugs;
an administration route comparator that generates a product-attribute similarity score based at least in part on the administration routes of the target and reference drugs;
a manufacturer comparator that generates a product-attribute similarity score based at least in part on the manufacturers of the target and reference drugs;
a pharmacological category comparator that generates a product-attribute similarity score based at least in part on the pharmacological categories of the target and reference drugs;
a storage requirements comparator that generates a product-attribute similarity score based at least in part on the storage requirements of the target and reference drugs;
a color comparator that generates an product-attribute similarity score based at least in part on the colors of the target and reference drugs;
a shape comparator that generates a product-attribute similarity score based at least in part on the shapes of the target and reference drugs;
a legal standing comparator that generates a product-attribute similarity score based at least in part on the legal standings of the target and reference drugs; and
a trademark description comparator that generates an product-attribute similarity to score based at least in part on the goods and services trademark descriptions of the target and reference drugs.
In some embodiments, the drug comparator includes a neighborhood density calculator that calculates a neighborhood density score for at least a first target drug of the selected target drugs based on a number of a first set of reference drugs determined by their being a distance from the first target drug that is not greater than a threshold distance. These distances are determined by comparing one or more attributes of the corresponding reference drug with corresponding one or more attributes of the first target drug. These attributes could be any name attributes, any product attributes, and/or any combination thereof. In these embodiments, the composite score generator generates the one or more composite quantitative comparisons based, at least in part, on the neighborhood density score.
Also, the drug comparator may include a neighborhood familiarity calculator that calculates a neighborhood familiarity score for at least a first target drug of the selected target drugs based on a familiarity of each of a first set of reference drugs. The distance between each of the first set of reference drugs and the first target drug is not greater than a threshold distance. These distances are determined by comparing one or more attributes of the corresponding reference drug with corresponding one or more attributes of the first target drug. These attributes could be any name attributes, any product attributes, and/or any combination thereof. In these embodiments, the composite score generator generates the one or more composite quantitative comparisons based, at least in part, on the neighborhood familiarity score. The confusability scores may be based at least in part on a first measure of familiarity that is based at least in part on the names of the first target drug and the names of the first set of reference drugs. Also, the first measure of familiarity may be determined by using frequency of prescription as a proxy for familiarity.
In yet further implementations, the drug comparator may include an attribute similarity scores processor that generates processed attribute similarity scores based at least in part on the attribute similarity scores. In these implementations, the composite score generator generates the composite quantitative comparisons based, at least in part, on the one or more attribute similarity scores by using the processed attribute similarity scores. In some aspects To of these implementations, at least one attribute similarity score is associated with a weight, and the attribute similarity scores processor generates the processed attribute similarity scores at least in part by determining a weighted sum of one or more of the attribute similarity scores. The weight of the at least one attribute similarity score may be any of a variety of type of weights. For example, the weight may be a predetermined real number, a predetermined exponential of the corresponding attribute similarity score, a real number that is a function of one or more attribute similarity scores, an exponential of the corresponding attribute similarity score that is a function of one or more attribute similarity scores, and so on. More generally, the weight may be calculated, selected, designated, or otherwise determined based on any mathematical function, logical function, or hybrid mathematical/logical function, including those derived analytically and/or those that may be learned from examples or by other adaptive methods. For convenience of reference, the phrase xe2x80x9ca type of weight determined by a mathematical/logical functionxe2x80x9d will be used herein to indicate any of these types of weights and/or combinations thereof.
The drug comparator may also include a severity of confusion scores generator that generates one or more severity of confusion scores representing a severity of confusion between the target and reference drugs. In these implementations, the composite score generator generates the composite quantitative comparisons based, at least in part, on the severity of confusion scores. In some aspects, a composite quantitative comparison may include severity-weighted similarity scores determined, at least in part, by weighting one or more of the processed attribute similarity scores by one or more of the severity of confusion scores. As used in this context, the term xe2x80x9cweightingxe2x80x9d includes modulating the processed attribute similarity scores by the severity of confusion scores in accordance with any known or later to be developed weighting technique including those that employ any mathematical and/or logical function. Also, the composite quantitative comparison may include a quantitative interpretation that compares a first set of one or more severity-weighted similarity scores corresponding to a first set of target and reference drugs to a second set of one or more severity-weighted similarity scores corresponding to a second set of target and reference drugs. The reference drugs of the first and second sets may be the same.
Furthermore, the composite quantitative comparison may include one or more processed attribute similarity scores and severity scores based on severity of confusion scores. In these implementations, the composite quantitative comparison may also include a quantitative interpretation that compares a first set of processed attribute similarity scores corresponding to a first set of target and reference drugs to a second set of processed attribute similarity scores corresponding to a second set of target and reference drugs. In some aspects, the composite quantitative comparison may have a quantitative interpretation that compares a first set of severity scores corresponding to a first set of target and reference drugs to a second set of severity scores corresponding to a second set of target and reference drugs.
In some implementations, the severity of confusion scores generator includes a therapeutic index comparator that generates a severity of confusion score based, at least in part, on a therapeutic index of one or more of the target and reference drugs. Also, the therapeutic index comparator may generate a severity of confusion scores based, at least in part, on a contraindication index of one or more of the target and reference drugs.
Typically, the composite quantitative comparisons include a measure of the absolute (as opposed to a solely relative) likelihood of confusing the selected target drugs with the selected reference drugs. That is, the composite quantitative comparisons provide a measure against one or more benchmark values of confusion. For example, a processed attribute similarity score (or, as explained below, a severity-weighted similarity score or un-processed attribute similarity score) of 0.5 may be provided for a target-reference pair of drugs A and B, and a similar type of score of 6.2 for the drugs A and C. These values may be compared to similar types of scores for large numbers of other target-reference pairs. Any of a variety of known statistical techniques may be applied to compare these two drug pairs to the larger population of drug pairs in terms of their respective scores. Thus, it may be determined, in accordance with these implementations, that drugs A and C not only are more likely to be confused than drugs A and B (a solely relative measure), but also that the score of 6.2 for drugs A and C represents a very high likelihood of confusion (what is referred to herein as an xe2x80x9cabsolutexe2x80x9d measure). This feature allows for more meaningful interpretation of comparison data than generally is the case with respect to conventional systems or methods.
In this example, the absolute measure may be determined based on a comparison of the drug pair consisting of drugs A and C with many, or even all, of the drug pairs that may be constructed from the target-reference data. Thus, it may be determined that the similarity, or likelihood of confusion, of drugs A and C is in the 99th percentile with respect to the likelihood of confusion as measured across a large population of target-reference pairs. As noted, this type of comprehensive comparison typically is not done in conventional systems because, among other reasons, the large number of comparisons makes the task impracticable in view of the time, and possibly expense, involved. Also, the measure of the likelihood of confusion determined in accordance with some embodiments may be based on one or more experimentally determined benchmarks. For example, experiments may have been conducted under controlled circumstances that indicate that the actual likelihood of confusing drugs A and C is very high as compared with the likelihood of confusing a number of other drug pairs. Thus, the similarity score of 6.2 for drugs A and C obtained in accordance with the preceding example may be identified as representing a value in the high range of confusability. Thus, even if no experimental data is available with respect to the likelihood of confusion of another target-reference pair of drugs A and E, they may be viewed as having a very high likelihood of confusion if they have a similarity score of 6.3. The similarity scores of drug pairs may also be scaled in accordance with one or more benchmarks of this type.
In other embodiments of the invention (referred to hereafter as xe2x80x9cmethodxe2x80x9d embodiments), a method is described for comparing one or more target drugs with one or more reference drugs based on attribute data that describes a plurality of attributes of each target drug and each reference drug. The method includes the steps of: (1) generating product-attribute similarity scores representing a similarity of product attributes of selected target and reference drugs; and (2) generating composite quantitative comparisons based, at least in part, on attribute similarity scores including the product-attribute similarity scores. In some implementations of this method, the composite quantitative comparisons are deterministic. In some aspects, this method may also include the step of (3) generating name-attribute similarity scores representing a similarity of name attributes of the selected target and reference drugs. In these aspects, the attribute similarity scores also include the name-attribute similarity scores. Other implementations and aspects of these methods generally parallel the implementations and aspects of the apparatus embodiments of the present invention, summarized above.
Yet other embodiments of the present invention are directed to a storage medium that contains software that, when executed on a computing system, performs a method for comparing one or more target drugs with one or more reference drugs based on attribute data that describes a plurality of attributes of each target drug and each reference drug. The methods practiced in accordance with various implementations of these embodiments may correspond to all or some of the methods of the methods embodiments, summarized above.
Further embodiments of the present invention are directed to a product that includes a composite quantitative comparison of one or more target drugs with one or more reference drugs based on attribute data that describes a plurality of attributes of each target drug and each reference drug. The composite quantitative comparison is generated by various methods that may correspond to all or some of the methods of the methods embodiments, summarized above.
Yet other embodiments of the present invention are directed to a computer-implemented system for comparing one or more target drugs with one or more reference drugs. The system includes (1) a target-reference data provider that provides target-reference data describing a plurality of attributes of each target drug and each reference drug; and (2) a composite analyzer that generates one or more composite quantitative comparisons of a selected one or more target drugs and a selected one or more reference drugs. The composite quantitative comparisons may be deterministic. The composite analyzer includes (a) an attribute similarity scores generator that generates one or more attribute similarity scores representing a similarity of attributes of the selected target and reference drugs. The attribute similarity scores generator includes any one or more attribute comparators selected from the group consisting of:
a strength comparator that generates an attribute similarity score based at least in part on the dosage strengths of the target and reference drugs;
an indication comparator that generates an attribute similarity score based at least in part on the indicated uses of the target and reference drugs;
a dosage form comparator that generates an attribute similarity score based at least in part on the dosage forms of the target and reference drugs;
an administration route comparator that generates an attribute similarity score based at least in part on the administration routes of the target and reference drugs;
a manufacturer comparator that generates an attribute similarity score based at least in part on the manufacturers of the target and reference drugs;
a pharmacological category comparator that generates an attribute similarity score based at least in part on the pharmacological categories of the target and reference drugs;
a storage requirements comparator that generates an attribute similarity score based at least in part on the storage requirements of the target and reference drugs;
a color comparator that generates an product-attribute similarity score based at least in part on the colors of the target and reference drugs;
a shape comparator that generates an attribute similarity score based at least in part on the shapes of the target and reference drugs;
a legal standing comparator that generates an attribute similarity score based at least in part on the legal standings of the target and reference drugs;
a trademark description comparator that generates an attribute similarity score based at least in part on the goods and services trademark descriptions of the target and reference drugs; or
a name attribute comparator that generates an attribute similarity score based at least in part on one or more names of the target and reference drugs.
In some implementations, the name attribute comparator includes any one or more name attribute analyzers selected from the group consisting of:
an orthographic analyzer that generates an attribute similarity score based at least in part on orthographic measures of the names of the selected target and reference drugs;
a phonetic analyzer that generates an attribute similarity score based at least in part on phonetic measures of the names of the target and reference drugs; or
a phonological analyzer that generates an attribute similarity score based at least in part on phonological measures of the names of the target and reference drugs.
The composite analyzer may also include a composite score generator that generates the composite quantitative comparisons based, at least in part, on the attribute similarity scores. In further implementations, the composite analyzer may also include a severity of confusion scores generator that generates severity of confusion scores representing a severity of confusion between the target and reference drugs. In these implementations, the composite score generator generates the composite quantitative comparisons based, at least in part, on the severity of confusion scores.
In yet further embodiments, the composite analyzer may also have a neighborhood density calculator that calculates a neighborhood density score for at least a first target drug of the selected target drugs based on a number of a first set of reference drugs determined by their being a distance from the first target drug that is not greater than a threshold distance. These distances are determined by comparing one or more attributes of the corresponding reference drug with corresponding one or more attributes of the first target drug. These attributes could be any name attributes, any product attributes, and/or any combination thereof. In these embodiments, the composite score generator generates the one or more composite quantitative comparisons based, at least in part, on the neighborhood density score.
Also, the composite analyzer may include a neighborhood familiarity calculator that calculates a neighborhood familiarity score for at least a first target drug of the selected target drugs based on a familiarity of each of a first set of reference drugs. The distance between each of the first set of reference drugs and the first target drug is not greater than a threshold distance. These distances are determined by comparing one or more attributes of the corresponding reference drug with corresponding one or more attributes of the first target drug. These attributes could be any name attributes, any product attributes, and/or any combination thereof. In these embodiments, the composite score generator generates the one or more composite quantitative comparisons based, at least in part, on the neighborhood familiarity score. The confusability scores may be based at least in part on a first measure of familiarity that is based at least in part on the names of the first target drug and the names of the first set of reference drugs. Also, the first measure of familiarity may be determined by using frequency of prescription as a proxy for familiarity.
In some embodiments, the computer system includes an attribute database updater that updates a target and reference drugs attribute database. This database includes data regarding attributes of the target and reference drugs. In these embodiments, the target-reference data provider provides the target-reference data based on the updated target and reference drugs attribute database.
In yet other embodiments, the invention is directed to a multi-attribute comparator for comparing one or more target items with one or more reference items based on attribute data that describes a plurality of attributes of each target item and each reference item. These target and reference items need not ordinarily pertain to pharmaceutical drugs or medically related communications. The multi-attribute comparator includes a product attribute comparator that generates one or more product-attribute similarity scores representing a similarity of product attributes of a selected one or more target items and a selected one or more reference items. The multi-attribute comparator also has a composite score generator that generates one or more composite quantitative comparisons based, at least in part, on one or more attribute similarity scores comprising the one or more product-attribute similarity scores. The one or more composite quantitative comparisons are deterministic.
A number of embodiments and implementations of the invention thus are possible, along with different aspects that may be included in each of the summarized embodiments. It should be understood that the embodiments, implementations, and/or aspects are not necessarily inclusive or exclusive of each other and may be combined in any manner that is non-conflicting and otherwise possible. It should also be understood that these embodiments, implementations, and/or aspects of the invention are exemplary only and are considered to be non-limiting.
As noted above, the present invention provides numerous advantages. Various embodiments, implementations, and/or aspects of the present invention provide certain advantages and overcome certain drawbacks of conventional techniques while other embodiments, implementations, and/or aspects provide the same or different advantages and overcome the same or other drawbacks in the same or different manner. Thus, not all embodiments, implementations, and/or aspects of the invention share the same advantages, and those that do may not share them under all circumstances.